文献类型：期刊文献

英文题名：Airborne P- and L-Band SAR Tomography for Forest Vertical Structure Mapping Using Only Four Images

作者：Wang, Longxiang[1,2] Xiang, Wei[1] Song, Hongjun[1] Zhang, Heng[1] Deng, Yunkai[1] Wang, Jili[1] Ji, Qilin[1] Zhao, Lei[3]

第一作者：Wang, Longxiang

通信作者：Xiang, W[1]

机构：[1]Chinese Acad Sci, Aerosp Informat Res Inst, Dept Space Microwave Remote Sensing Syst, Beijing 100094, Peoples R China;[2]Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China;[3]Chinese Acad Forestry, Res Inst Forest Resources Informat Tech, Beijing 100091, Peoples R China

年份：2025

卷号：63

外文期刊名：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

收录：;EI(收录号：20254219347519);Scopus(收录号：2-s2.0-105018823821);WOS:【SCI-EXPANDED(收录号:WOS:001626459000007)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62301535 and Grant 62201548.

语种：英文

外文关键词：Forestry; Tomography; Estimation; Iterative methods; Three-dimensional displays; Synthetic aperture radar; Accuracy; Superresolution; L-band; Robustness; 3-D forest mapping; forest vertical structure (FVS); L band; P band; synthetic aperture radar tomography (TomoSAR)

摘要：Synthetic aperture radar (SAR) tomography (TomoSAR) technology effectively provides precise 3-D forest vertical structure (FVS) information. However, conventional TomoSAR methods require abundant acquisitions for accurate 3-D reconstruction, which is time-consuming and low-efficiency for FVS mapping. To address these limitations, this article proposes a novel microstack TomoSAR imaging approach utilizing four images, referred to as the double iterative adaptive residual approach (DIARA). The DIARA innovatively combines iterative inner-outer adaptive spectral estimation and residual optimization to enhance both processing efficiency and accuracy. For validation purposes, both simulated and airborne TomoSAR experiments are analyzed at P and L bands. The P-band results from the BorTomoSAR campaign indicate that the DIARA acquires higher accuracy for estimating forest height than the conventional methods, i.e., improving R-2 from 0.443 to 0.628, mean absolute error (MAE) from 0.779 to 0.625 m, mean absolute percentage error (MAPE) from 4.629% to 3.680%, and root mean square error (RMSE) from 0.941 to 0.771 m. In addition, the performance is further validated by the TropiSAR P-band campaign, which confirms the robustness of the proposed DIARA in high-canopy, complex forest environments. Furthermore, the Lband results from HaiTomoSAR campaign demonstrate that the DIARA method successfully detects the weak ground scatterers beneath dense forest canopies, which validates its super-resolution capability in vertical structure reconstruction.